At Rise of AI 2023, Hayri Goecke, Head of Growth at Gestalt Robotics, presents how his company applies computer vision and machine learning to automate maintenance for safety-critical mobility operations, focused on the E-Check project with Deutsche Bahn. Founded in 2016 and grown to 60 people without venture capital, Gestalt Robotics builds software modules that run on hardware from vendor-agnostic partners across computer vision, mobile robotics, arm robotics and worker-assistance systems. Goecke explains that Deutsche Bahn's growing train fleet is roughly doubling maintenance needs while a new maintenance site costs about 400 million euros to build, so the company is instead digitizing five existing sites with a 32-camera gate that scans each ICE train's underside at about 5 km/h, producing roughly 6,500 images and 30 gigabytes of data per wagon. He describes the technical challenge of producing precise, reproducible images across different trains, the on-site edge computing and cloud processing behind the system, and the close but clearly defined supplier relationship with Deutsche Bahn, including regular scrum-based feedback.
Key takeaways
- Gestalt Robotics has grown to 60 people without venture capital by building software modules for computer vision and machine learning that run on vendor-agnostic hardware.
- Deutsche Bahn's E-Check project digitizes five existing maintenance sites instead of building new ones, which Goecke says cost about 400 million euros each.
- The E-Check camera gate uses 32 cameras and speed-related mapping to produce about 6,500 reproducible images, roughly 30 gigabytes, per train wagon.
- Processing combines 16 on-site edge computers with cloud computing, AI detection, human verification and supporting robotic tasks.
- Goecke describes Gestalt Robotics' role with Deutsche Bahn as a clear supplier relationship, won through public tender, kept close through shared scrum events rather than joint ownership.
Detailed notes
Gestalt Robotics: a software company built on hardware
Goecke introduces Gestalt Robotics, founded in 2016 and grown to 60 people without venture capital funding, working with clients across mobility, discrete manufacturing, chemicals and pharma in Germany, elsewhere in Europe, and in the US and Canada. Although named for robotics, he stresses the company is software-driven, building networked and virtualized modules for computer vision and machine learning on the shop floor, and staying hardware-agnostic by working with partners such as UR robots or FANUC for the physical systems, alongside Verizon in the US for edge-computing deployments.
Four solution fields and early use cases
Goecke groups the company's work into computer vision, autonomous mobile robotics, arm robotics, and worker-assistance systems. Examples include 'navigate this,' a mobile robotics platform built with Verizon that uses 5G and edge computing so a camera-equipped robot can detect objects like pallets and feed that data into a factory's digital twin or SAP system; a laboratory automation system for a chemical client where two UR robots collaboratively open sample vials and use visual servoing to pipette liquid into small tubes; an augmented-reality assistant that guides a worker through tasks using a pre-scanned digital twin; and a quality gate developed together with Siemens that uses few-shot learning, trained on five to ten images, to check the configuration of assembled medium voltage devices, replacing two of the four eyes in a manual four-eye inspection process.
The E-Check challenge: maintaining a growing rail fleet
The main project, E-Check, addresses a capacity problem at Deutsche Bahn: Goecke says a fleet growth of 250 trains roughly doubles the maintenance need, while the workforce able to perform that maintenance is aging, and building a single new maintenance site costs around 400 million euros. Rather than build new sites, Deutsche Bahn is digitizing five existing ones, aiming for full surface inspection coverage of trains and a solution that works in both new-build and existing (brownfield) sites.
How the camera gate works
The core of E-Check is a camera gate with 32 cameras that scans an entire ICE train's underside as it drives through at roughly 5 km/h, producing about 6,500 images and 30 gigabytes of data per wagon. Goecke says the central technical challenge is producing precise, reproducible images, achieved through speed-related mapping that ensures the same parts are photographed consistently across different trains, plus coping with how rarely serious faults, such as a broken ICE wheel, actually occur in the data. Processing happens through 16 on-site edge computers feeding into the cloud, combined with AI detection, human verification, and supporting robotic tasks such as exchanging dirty and clean water on the train, handled by a robotics partner.
Rollout and the supplier relationship with Deutsche Bahn
A prototype camera gate is already installed in Munich, and Gestalt Robotics is rolling out to five sites across Germany with seven gates in total, some sites getting two. The project came from a public tender that Gestalt Robotics won together with a partner company, and Goecke describes the relationship as a clear supplier arrangement rather than shared ownership, though the teams work closely together, with Deutsche Bahn's own AI team participating in Gestalt Robotics' scrum events to give fast feedback. He says the company is working with a Deutsche Bahn subsidiary to turn E-Check into a broader 'smart assistant maintenance' product for high-speed rail operators internationally.
Q&A highlights
Asked what consistent core IP underlies the company's varied projects, Goecke points to the capability to set up and individualize such systems for a specific industry, underpinned by a shared MLOps pipeline used across projects, such as laboratory automation for any lab or the camera-gate approach for any high-speed rail operator. Asked to characterize the Deutsche Bahn relationship, he reiterates it is a clear supplier relationship won through public tender rather than a shared 'workbench,' but with tight day-to-day collaboration through joint scrum events so that feedback is immediate rather than delivered only after years of separate development.
Quotes
We are software driven, so what we built are technological modules that are highly networked and virtualized.
Hayri Goecke
A fleet growth of 250 trains doubles the maintenance need for Deutsche Bahn, roughly.
Hayri Goecke
For us, data acquisition is the key here, it's way more about acquiring the data and processing the data than in the end about the AI.
Hayri Goecke
Questions answered in this talk
What is the E-Check project with Deutsche Bahn?
According to Hayri Goecke at Rise of AI 2023, E-Check is a Gestalt Robotics project with Deutsche Bahn that digitizes train maintenance using a 32-camera gate that scans each ICE train's underside at about 5 km/h, producing roughly 6,500 images per wagon for AI-assisted inspection.
Why is Deutsche Bahn digitizing existing maintenance sites instead of building new ones?
According to Hayri Goecke at Rise of AI 2023, a growing fleet is roughly doubling Deutsche Bahn's maintenance needs, while a new maintenance site costs around 400 million euros to build, making digitizing five existing sites the more economical path.
What is Gestalt Robotics' relationship with Deutsche Bahn?
According to Hayri Goecke at Rise of AI 2023, Gestalt Robotics is a supplier to Deutsche Bahn, having won the E-Check contract through a public tender, working in tight collaboration through joint scrum events rather than as a co-owner of the project.